In the coming decades, it is expected that mankind will have to double the production of food crops in order to meet global food demand. Research in plant phenomics, in particular in relation to deep plant phenotyping and reverse phenomics, assists in understanding the metabolism and physiological processes of plants, and helps to guide development of more efficient and resistant crops for tomorrow's agriculture.
Discovery of new traits to increase potential yield in crops relies on screening large germplasm collections for suitable characteristics. As noted in the study of Eberius and Lima-Guerra (Bioinformatics 2009, 259-278), high-throughput plant phenotyping requires acquisition and analysis of data for several thousand plants per day. Data acquisition may involve capture of high-resolution stereographic, multi-spectral and infra-red images of the plants with the aim of extracting phenotypic data such as main stem size and inclination, branch length and initiation angle, and leaf width, length, and area. Traditionally, these phenotypic data have been derived from manual measurements, requiring approximately 1 hour per plant depending on its size and complexity. Manual analysis of this type for large numbers of plants is impractical and the development of automated solutions is therefore called for.
Previous approaches to automation of phenotypic data extraction include the PHENOPSIS software of Granier et al (New Phytologist 2006, 169(3):623-635) and GROWSCREEN of Walter et al (New Phytologist 2007, 174(2):447-455). These are semi-automated approaches which employ 2D image processing to extract phenotypic data for leaves (leaf width, length, area, and perimeter) and roots (number of roots, root area, and growth rate).
Another approach is implemented in LAMINA of Bylesjö et al (BMC Plant Biology 2008, 8:82), another 2D image processing tool which is capable of extracting leaf shape and size for various plant species.
A further approach which works well for observation of root phenotypic traits such as number of roots, average root radius, root area, maximum horizontal root width, and root length distribution is implemented in RootTrace (Naeem et al, Bioinformatics. 2011, 27(9):1337; Iyer-Pascuzzi et al, Plant Physiology 2010, 152(3):1148) in which 2D image analysis is used to extract leaves and roots data.
Yet further approaches to automated phenotyping include the analysis of three-dimensional surface meshes constructed from stereographic images. For example, GROWSCREEN 3D (Biskup et al, Plant Physiology 2009, 149(3):1452) implements this approach for analysis of leaf discs, while RootReader3D (Clark et al, Plant Physiology 2011, 156(10):455-465) does likewise for roots. A three-dimensional approach allows more accurate automated measurements of leaf area, and the extraction of additional data such as root system volume, surface area, convex hull volume, or root initiation angles.
A disadvantage of each of the aforementioned approaches is that each is optimized for a particular plant organ or system such as leaves or roots, and does not provide a mechanism for studying the phenotype of the plant as a whole.
In addition, many plants have complex and irregular morphology. The present inventors have found that no generic mesh segmentation algorithm is robust enough to automatically and accurately identify the different plant parts (e.g. main stem, branches, leaves).
It would be desirable to alleviate or overcome one or more of the above difficulties, or at least to provide a useful alternative.